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Xuejie Qin

dblp:29/2331 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 4 · 4 first-authorArtificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
3 papers
Visual content generation and editing · 34% Multimedia analysis and retrieval · 26% Rendering · 25%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing
texture synthesis
0.122007
Aura 3D Textures · IEEE Trans. Vis. Comput. Graph. 2007
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis · ICCV 2005
Rendering › texture mapping
solid texture
0.112007
Aura 3D Textures · IEEE Trans. Vis. Comput. Graph. 2007
Image and video processing
texture analysis
0.112005
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis · ICCV 2005
Multimedia analysis and retrieval
image retrieval
0.012004
Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval · CVPR (1) 2004
Multimedia analysis and retrieval › image retrieval › content-based image retrieval
texture retrieval
0.012004
Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval · CVPR (1) 2004
Rendering
texture mapping
0.012007
Aura 3D Textures · IEEE Trans. Vis. Comput. Graph. 2007

Methods — techniques the papers use, named apart from their topics

gray level aura matrices · 0.1multi-view sampling · 0.1aura matrix representation · 0.1gray-level co-occurrence matrix · 0.1
YearPublicationVenuePosition
2007 Aura 3D Textures
abstract
This paper presents a new technique, called aura 3D textures, for generating solid textures based on input examples. Our method is fully automatic and requires no user interactions in the process. Given an input texture sample, our method first creates its aura matrix representations and then generates a solid texture by sampling the aura matrices of the input sample constrained in multiple view directions. Once the solid texture is generated, any given object can be textured by the solid texture. We evaluate the results of our method based on extensive user studies. Based on the evaluation results using human subjects, we conclude that our algorithm can generate faithful results of both stochastic and structural textures with an average successful rate of 76.4 percent. Our experimental results also show that the new method outperforms Wei and Levoy's method and is comparable to that proposed by Jagnow et al. [21].
Xuejie Qin, Yee-Hong Yang
IEEE Trans. Vis. Comput. Graph.1
2005 Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis
abstract
In this paper, we present a new mathematical framework for modeling texture images using independent basic gray level aura matrices (BGLAMs). We prove that independent BGLAMs are the basis of gray level aura matrices (GLAMs), and that an image can be uniquely represented by its independent BGLAMs. We propose a new BGLAM distance measure for automatically evaluating synthesis results w.r.t. input textures to determine if the output is a successful synthesis of the input. For the application to texture synthesis, we present a new algorithm to synthesize textures by sampling only the independent BGLAMs of an input texture. With respect to synthesis of textures and evaluation of the results, the performance of our approach is extensively evaluated and compared with symmetric GLAMs that are used in existing techniques and with gray level cooccurrence matrices (GLCMs). Experimental results have shown that (1) our approach significantly outperforms both symmetric GLAMs and GLCMs; (2) the new BGLAM distance measure has the ability to evaluate synthesis results, which can be used to automate the conventional visual inspection process for determining whether or not the output texture is a successful synthesis of the input; and (3) a broad range of textures can be faithfully synthesized using independent BGLAMs and the synthesis results are comparable to existing techniques.
Xuejie Qin, Yee-Hong Yang
ICCV1
2004 Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval
Xuejie Qin, Yee-Hong Yang
CVPR (1)1
2003 Automatic Extraction of Clusters from Hierarchical Clustering Representations
Jörg Sander 0001, Xuejie Qin, Zhiyong Lu, Nan Niu, Alex Kovarsky
PAKDD2
2002 Estimating Parameters for Procedural Texturing by Genetic Algorithms
Xuejie Qin, Yee-Hong Yang
Graph. Model.1